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Several Formulations for Graded Possibilistic Approach to Fuzzy Clustering

  • Katsuhiro Honda
  • Hidetomo Ichihashi
  • Akira Notsu
  • Francesco Masulli
  • Stefano Rovetta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

Fuzzy clustering is a useful tool for capturing intrinsic structure of data sets. This paper proposes several formulations for soft transition of fuzzy memberships from probabilistic partition to possibilistic one. In the proposed techniques, the free memberships are given by introducing additional penalty term used in Possibilistic c-Means. The new features of the proposed techniques are demonstrated in several numerical experiments.

Keywords

Fuzzy Cluster Fuzzy Membership Soft Transition Possibilistic Approach Probabilistic Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Katsuhiro Honda
    • 1
  • Hidetomo Ichihashi
    • 1
  • Akira Notsu
    • 1
  • Francesco Masulli
    • 2
  • Stefano Rovetta
    • 2
  1. 1.Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan
  2. 2.Department of Computer and Information SciencesUniversity of Genova and CNISMGenovaItaly

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